我会将这些参数加载到Python脚本中,如下所示: importyamlwithopen("config.yaml",'r')asconfig_file:config=yaml.safe_load(config_file)image=cv2.imread(config['image_path'],cv2.IMREAD_GRAYSCALE)noisy_image=add_gaussian_noise(image,config['mean'],config['variance'])cv2.imwrite(config['output_path...
AI检测代码解析 cv2.imwrite('noisy_image.jpg',cv2.cvtColor(noisy_image,cv2.COLOR_RGB2BGR))# 保存添加噪音后的图像,转换颜色通道为BGR 1. 流程与状态图 下面是流程的序列图以及状态图,帮助你更好地理解整个过程。 PythonDeveloperPythonDeveloperImport librariesRead imageGenerate Gaussian noiseAdd noise to ima...
1、https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv# 2、https://stackoverflow.com/questions/14435632/impulse-gaussian-and-salt-and-pepper-noise-with-opencv# __EOF__...
img= ...noise= ...image= img + noise AI代码助手复制代码 参考链接: 1、https://stackoverflow.com/questions/22937589/how-to-add-noise-gaussian-salt-and-pepper-etc-to-image-in-python-with-opencv# 2、https://stackoverflow.com/questions/14435632/impulse-gaussian-and-salt-and-pepper-noise-with-...
shimage.util.random_noise(image,mode='gaussian',seed=None,clip=True) 注意事项: Peckle, Poisson, Localvar, and Gaussian noise 加上噪声后,值可能为负值,也可能超过255;默认情况下,clip参数值为True,将会clip掉这些超过区间的点,如果clip设置为False,就要注意有可能包含一些超过区间的点。
Due to clipping in random_noise, the estimate will be a bit smaller than the specified sigma. --- input: noisy pic output: estimated sigma ''' sigma_est = estimate_sigma(noisy, channel_axis=-1, average_sigmas=True) print(f'Estimated Gaussian noise standard deviation = {sigma_est}') ...
fromPILimportImagefromPILimportImageEnhanceimportos'''函 数名:contrastEnhancement(root_path, img_name, contrast)函数功能:对比度增强入口参数:root_path :图片根目录img_name :图片名称contrast :对比度返 回值:对比度增强后的图片'''defcontrastEnhancement(root_path,img_name,contrast):image=Image.open(os....
image = cv2.GaussianBlur(image, ksize=(degree, degree), sigmaX=0, sigmaY=0) 3) 噪点 其实就是在每个像素点添加随机扰动: defgaussian_noise(image, degree=None): row, col, ch = image.shape mean =0ifnotdegree: var = np.random.uniform(0.004,0.01)else: ...
import cv2import numpy as npfrom imgaug import augmenters as iaaseq=iaa.Sequential([iaa.Crop(px=(0,30)),iaa.Fliplr(0.7),iaa.GaussianBlur(sigma=(0,2.0)),iaa.Dropout(0.3),iaa.Grayscale(0.9),iaa.Emboss(0.9),iaa.EdgeDetect(0.5),iaa.AdditiveGaussianNoise(loc=0,scale=50),iaa.Multiply(2)...
在本节中,我们将演示如何使用 scikit image 的形态学模块中的函数来实现一些形态学操作,首先对二值图像进行形态学操作,然后对灰度图像进行形态学操作。 二进制运算 让我们从二值图像的形态学操作开始。在调用函数之前,我们需要创建一个二进制输入图像(例如,使用具有固定阈值的简单阈值)。 腐蚀 侵蚀是一种基本的形态...